Being able to detect the occurrence and the specific nature of object-level changes that occur within a scene (e.g., a physical location) may be useful for many remote sensing applications. Such applications may include detecting new construction and building alterations, or performing other remote sensing. For example, within a scene it may be useful to detect the appearance of a new object, the disappearance of a pre-existing object, or movement or other changes related to a particular object.
Known approaches for detecting differences between images of a common scene typically utilize pixel-based analysis and/or analysis of spectral images. Pixel-based analysis sometimes fails to identify object-level changes within a scene and generally is not useful in determining the specific nature of such object-level changes. That is, pixel-based analysis might be useful to determine that a new object has appeared within the scene, but might not be useful in determining that a new house has been built at the scene. Pixel-based analysis is also often complicated by differing capture conditions of the images under analysis, such as differences in illumination or cloud cover.
In addition, useful comparison of spectral images typically requires that the spectral images were captured by the same sensor, identical sensors, or very similar sensors. For example, a first image captured by a red-green-blue (RGB) sensor and a second image captured by an infrared sensor would be incompatible for pixel-by-pixel comparison because the intensity and colors depicted in an RGB image might not have the same meaning as the same intensity and colors depicted in an infrared image.
Accordingly, there is a need for systems and methods for improving detection and characterization of object-level changes that occur within in a scene.